You’re On Your Own Kid, You Always Have Been: An Institutional Ethnography for Supporting First-In-Family Women Attending Ontario Universities
Bibliographic record
Abstract
James & Taylor (2023, p.14) find that first-generation university students are defined by their relation to an educational system designed to serve an “outdated model of the typical student.” Students who are first in their family to go to university are particularly placed at- risk as a diverse cohort, often intersected by multiple equity categories, such as low socioeconomic status (SES), Indigeneity, disability, and/or racialization, who experience compounding disadvantages in educational settings (Groves & O’Shea, 2019; O’Shea et al., 2018) that are heightened without adequate/relevant guidance or support from immediate family members. The purpose of this research was to illuminate systematic structural barriers that materialized in institutional texts and continue to perpetuate inequitable ruling relations representative of an “old boys’ network” (Henry et al., 2017, p.290): white, western, male, middle class, heterosexual, able bodies. I investigated how hegemonic centres of universities perpetuate deficit discourses that situate marginalized women who are first-in-family students in this disadvantaged position, why such students are responsible for accommodating to meet academic and institutional standards, and how they know. Using Dorothy Smith’s institutional ethnography as my method of inquiry, with attention to critical feminist pedagogies, I analyzed Queen’s University in Ontario, Canada as a textually mediated institutional site, and revealed the existing ruling relations that subject first-in-family students to obstacles to their education and overall quality of student life. First-in-family students must “bend” to accommodate these obstacles and the disjunctures they create. Informant narrative accounts informed the direction of this institutional ethnography. This institutional ethnography revealed how local ruling relations are textually mediated, that they burden marginalized first-in-family university students, and that universities exploit first-in-family students in their failure to provide structures that position them in the institution to equitably derive the benefits of higher education.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.026 | 0.025 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".